Linear Mixed Models for Longitudinal Data / Edition 1

Linear Mixed Models for Longitudinal Data / Edition 1

by Geert Verbeke, Geert Molenberghs
     
 

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ISBN-10: 0387950273

ISBN-13: 9780387950273

Pub. Date: 06/16/2000

Publisher: Springer New York

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive

Overview

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogenity model, conditional linear mid models).

Product Details

ISBN-13:
9780387950273
Publisher:
Springer New York
Publication date:
06/16/2000
Series:
Springer Series in Statistics
Edition description:
1st ed. 1997. Corr. 2nd printing 2001
Pages:
568
Product dimensions:
6.00(w) x 9.30(h) x 1.30(d)

Table of Contents

A Model for Longitudinal Data.

Exploratory Data Analysis.

Estimation of the Marginal Model.

Inference for the Marginal Model.

Inference for the Random Effects.

Fitting Linear Mixed Models with SAS.

General Guidelines for Model Building.

Exploring Serial Correlation.

Local Influence for the Linear Mixed Model.

The Heterogeneity Model.

Conditional Linear Mixed Models.

Exploring Incomplete Data.

Joint Modeling of Measurements and Missingness.

Simple Missing Data Methods.

Selection Models.

Pattern-Mixture Models.

Sensitivity Analysis for Selection Models.

Sensitivity Analysis for Models.

How Ignorable is Missing at Random?

The Expectation-Maximization Algorithm.

Design Considerations.

Case Studies.

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